Search Results (146)

Multilevel thresholding image segmentation plays a crucial role in various image processing applications. However, achieving optimal segmentation results often poses challenges due to the intricate nature of images. In this study, a novel metaheuristic search algorithm named Weighted Chimp Optimization Algorithm with Fitness–Distance Balance (WChOA-FDB) is developed. The algorithm integrates the concept of Fitness–Distance Balance (FDB) to ensure balanced exploration and exploitation of the solution space, thus enhancing convergence speed and solution quality. Moreover, WChOA-FDB incorporates weighted Chimp Optimization Algorithm techniques to further improve its performance in handling multilevel thresholding challenges. Experimental studies were conducted to test and verify the developed method. The algorithm’s performance was evaluated using 10 benchmark functions (IEEE_CEC_2020) of different types and complexity levels. The search performance of the algorithm was analyzed using the Friedman and Wilcoxon statistical test methods. According to the analysis results, the WChOA-FDB variants consistently outperform the base algorithm across all tested dimensions, with Friedman score improvements ranging from 17.3% (Case-6) to 25.2% (Case-4), indicating that the FDB methodology provides significant optimization enhancement regardless of problem complexity. Additionally, experimental evaluations conducted on color image segmentation tasks demonstrate the effectiveness of the proposed algorithm in achieving accurate and efficient segmentation results. The WChOA-FDB method demonstrates significant improvements in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM) metrics with average enhancements of 0.121348 dB, 0.012688, and 0.003676, respectively, across different threshold levels (m = 2 to 12), objective functions, and termination criteria. Full article

Weeds present a pervasive challenge in agricultural fields. The integration of herbicide-resistant crops with chemical weed management offers an effective solution for sustainable weed control while reducing labor inputs, particularly in large-scale intensive farming systems. Consequently, the development of herbicide-resistant cultivars has emerged as an urgent priority. In this study, we found that the G311E mutant gene of Arabidopsis MATE (multidrug and toxic compound extrusion) family transporter DTX6, designated DTX6m, confers robust resistance to bipyridyl herbicides paraquat and diquat in rice. DTX6m-overexpression lines exhibited marked resistance to these two herbicides, tolerating diquat concentrations up to 5 g/L, which is five-fold higher than the recommended field application dosage. Agronomic assessments demonstrated that grain yields of DTX6m-overexpressing plants were statistically equivalent to those of wild-type plants. Moreover, the plants displayed beneficial phenotypic changes, such as accelerated flowering and a slight reduction in height. Seed morphometric analysis indicated that in comparison with the wild-type control, DTX6m-transgenic lines exhibited altered grain dimensions while maintaining consistent 1000-grain weight. Nutritional assays further demonstrated that DTX6m increased the levels of free amino acids in seeds, while normal protein and starch contents were retained. Collectively, these results establish that DTX6m effectively boosts rice resistance to paraquat and diquat, validating DTX6m as a candidate gene for engineering plant herbicide resistance and also implying a potential role for DTX6m in amino acid homeostasis in plants. Full article

Urban climate resilience is shaped by both direct exposure to environmental risks and cognitive, socioeconomic and institutional factors. This study investigates climate change risk perception (CCRP), psychological distance (PD) and adaptive capacity (AC) across five districts of Istanbul: Beşiktaş, Kadıköy, Kağıthane, Şişli and Üsküdar, using a structured survey (sample size = 500) and advanced multivariate statistical modeling to explore the factors influencing adaptive behavior. To evaluate perceptual and behavioral responses to climate threats, the study constructs both equal-weighted indices and indices derived through principal component analysis (PCA). ANOVA and chi-square tests reveal significant district-level differences in risk perception and adaptation engagement. PCA results validate the internal structure of the indices by identifying latent dimensions such as institutional confidence, emotional proximity and self-efficacy. Correlation and regression analyses confirm that CCRP and PD significantly predict AC in theoretically meaningful patterns. Structural equation modeling (SEM) demonstrates both direct and indirect pathways linking climate risk perception to adaptive capacity, highlighting the complex interplay of these variables. Mediation analysis shows that PD partially mediates the CCRP–AC relationship, accounting for 39.7% of the total effect. Cluster analysis identifies distinct cognitive profiles where proactive adaptation behaviors are more common in affluent districts while disengagement is more prevalent in low-income areas. These findings underscore the importance of localized communication efforts, institutional credibility and financial equity in shaping effective climate adaptation. By integrating perceptual and structural dimensions, the study advances a multidimensional understanding of urban climate readiness and offers empirical guidance for socially equitable resilience policy design. Full article

Understanding the nonlinear relationship between human activity intensity (HAI) and ecosystem resilience (ER) is crucial for sustainability, yet underdeveloped areas are often overlooked. This study examines the Xuzhou Urban Agglomeration (XZUA) from 2012 to 2022, creating a framework to assess both ER and HAI. Both frameworks utilize multi-source datasets, such as remote sensing, statistical yearbooks, and geospatial data. The ER framework uniquely combines dynamic and static indicators, while the HAI framework differentiates explicit and implicit human activity dimensions. We used spatial analysis, the Optimal Parameter Geodetector (OPGD), and Multi-Scale Geographically Weighted Regression (MGWR) to examine the nonlinear spatiotemporal interaction between HAI and ER. Results show the following: (1) ER exhibited a “shock-recovery” pattern with a net decline of 3.202%, while HAI followed a nonlinear “rise-fall” trend with a net decrease of 0.800%. (2) Spatial mismatches between HAI and ER intensified over time. (3) The negative correlation in high-HAI regions remained stable, whereas neighboring low-HAI areas deteriorated, indicating a spillover effect. (4) OPGD identified the change in HAI (Sen’s slope) as the primary driver of ER change (q = 0.512), with the strongest interaction observed between HAI Sen’s slope and precipitation (q = 0.802). (5) Compared to HAI intensity (mean), its temporal variation had a more spatially stable influence on ER. These findings offer insights for ecological management and sustainable planning in underdeveloped regions, highlighting the need for targeted HAI and ER interventions. Full article

Flood hazards caused by intense short-term precipitation have led to significant social and economic losses and pose serious threats to human life and property. Accurate disaster risk assessment plays a critical role in verifying disaster statistics and supporting disaster recovery and reconstruction processes. In this study, a novel Large-Scale Flood Risk Assessment Model (LS-FRAM) is proposed, incorporating the dimensions of hazard, exposure, vulnerability, and coping capacity. Multi-source heterogeneous data are utilized for evaluating the flood risks. Soil erosion modeling is incorporated into the assessment framework to better understand the interactions between flood intensity and land surface degradation. An index system comprising 12 secondary indicators is constructed and screened using Pearson correlation analysis to minimize redundancy. Subsequently, the Analytic Hierarchy Process (AHP) is utilized to determine the weights of the primary-level indicators, while the entropy weight method, Fuzzy Analytic Hierarchy Process (FAHP), and an integrated weighting approach are combined to calculate the weights of the secondary-level indicators. This model addresses the complexity of large-scale flood risk assessment and management by incorporating multiple perspectives and leveraging diverse data sources. The experimental results demonstrate that the flood risk assessment model, utilizing multi-source data, achieves an overall accuracy of 88.49%. Specifically, the proportions of areas classified as high and very high flood risk are 54.11% in Henan, 31.74% in Shaanxi, and 18.2% in Shanxi. These results provide valuable scientific support for enhancing flood control, disaster relief capabilities, and risk management in the middle and lower reaches of the Yellow River. Furthermore, they can furnish the necessary data support for post-disaster reconstruction efforts in impacted areas. Full article

For manoeuvring surface target tracking in the presence of glint noise, Huber-based Kalman filters have been widely regarded as effective. However, when the proportion of outlier measurements is high, their numerical stability and estimation accuracy can deteriorate significantly. To address this issue, we propose a Robust Cubature Kalman Filter with the Current Statistical (RCKF_CS) model. Inspired by the Huber equivalent weight function, an adaptive factor incorporating a penalty strategy based on a smoothing approximation function is introduced to suppress the adverse effects of glint noise. The proposed method is then integrated into the Cubature Kalman Filter framework combined with the Current Statistical model. Unlike conventional Huber-based approaches, which process measurement residuals independently in each dimension, the proposed method evaluates the residuals jointly to improve robustness. Numerical stability analysis and extensive simulation experiments confirm that the proposed RCKF_CS achieves improved numerical robustness and filtering performance, even under strong glint noise conditions. Compared with existing Huber-based filters, the proposed method enhances filtering performance by 2.66% to 10.18% in manoeuvring surface target tracking tasks affected by glint noise. Full article

Promoting the comprehensive green transformation (CGT) of China’s economy and society is vital for achieving high-quality economic growth and building a beautiful China. This study establishes a CGT evaluation index system across four dimensions: comprehensiveness, synergy, innovation, and security. Using the entropy weighting method, it evaluates the CGT development level across 30 Chinese provinces from 2011 to 2022. Subsequently, it examines regional differences and convergence features in CGT development through the application of the Theil index, kernel density estimation (replaced by KDE below), and convergence analysis methods. The findings indicate the following: Firstly, the CGT development level nationwide and within the four key regions has been on the rise annually; yet, regional variations persist. Secondly, both the overall disparities in CGT development across China’s economy and society and the discrepancies within the four key regions are diminishing. Furthermore, interregional variations are the main contributor to the overall disparities in CGT development. Thirdly, while the number of provinces achieving CGT development has gradually increased nationwide, their unevenness has also intensified. Fourthly, regarding convergence characteristics, σ-convergence, along with both absolute and conditional β-convergence are observed in all regions but the central; in the central region, absolute β-convergence is not statistically significant, but conditional β-convergence is. Conclusions from this study can offer theoretical insights for further elevating the CGT level of China’s economy and society and fostering coordinated regional development. Full article

Traditional villages are vital repositories of China’s historical and cultural heritage. To enhance protection precision, this study develops a novel risk assessment framework integrating three dimensions: the natural environment, tangible heritage elements, and disaster prevention infrastructure. The framework mainly uses GIS spatial analysis and SPSS-based statistical modeling. It integrates traditional dwelling density as a key factor in vulnerability zoning by depicting assessment units with weighted vulnerability indicators. The study overlays kernel density maps of traditional buildings with natural hazard susceptibility data. This enables classification of villages and clusters into hierarchical disaster prevention tiers (core, key, and general zones). Core zones, characterized by high-density heritage structures and elevated flood risks, require structural reinforcement and ecological engineering, while key zones employ adaptive protection technologies. By incorporating traditional building density as a weighted vulnerability indicator, the framework enables hierarchical disaster zoning through spatial coupling of kernel density maps and flood susceptibility data. Taking the results of Lingshui Village as an example, an individual analysis was made, and the elements of the village were identified. Fourteen traditional villages in Mentougou District were graded and partitioned. Correlation examination of zoning findings and property damage, as well as an independent evaluation of categorization results and degree of calamity, demonstrated a correlation between the two. Therefore, empirical validation in Beijing’s Mentougou District demonstrates the efficacy of this approach. The methodology further establishes cross-village collaborative defense mechanisms under a “conservation–development–protection” paradigm, aligning administrative boundaries with spatial agglomeration patterns. The study establishes a hierarchical disaster prevention evaluation system and a regional technical pathway to bridge individual and cluster-level protection. Finally, by synergizing traditional dwelling conservation with ecological resilience, it explores bidirectional optimization between cultural heritage preservation and disaster prevention efficacy. Full article

To enable accurate and efficient real-time detection of rail fastener defects under resource-constrained environments, we propose DP-YOLO, an advanced lightweight algorithm based on YOLOv5s with four key optimizations. First, we design a Depthwise Separable Convolution Stage Partial (DSP) module that integrates depthwise separable convolution with a CSP residual connection strategy, reducing model parameters while enhancing recognition accuracy. Second, we introduce a Position-Sensitive Channel Attention (PSCA) mechanism, which calculates spatial statistics (mean and standard deviation) across height and width dimensions for each channel feature map. These statistics are multiplied across corresponding dimensions to generate channel-specific weights, enabling dynamic feature recalibration. Third, the Neck network adopts a GhostC3 structure, which reduces redundancy through linear operations, further minimizing computational costs. Fourth, to improve multi-scale adaptability, we replace the standard loss function with Alpha-IoU, enhancing model robustness. Experiments on the augmented Roboflow Universe Fastener-defect-detection Dataset demonstrate DP-YOLO’s effectiveness: it achieves 87.1% detection accuracy, surpassing the original YOLOv5s by 1.3% in mAP0.5 and 2.1% in mAP0.5:0.95. Additionally, the optimized architecture reduces parameters by 1.3% and computational load by 15.19%. These results validate DP-YOLO’s practical value for resource-efficient, high-precision defect detection in railway maintenance systems. Full article

This study presents a methodological framework for integrating LCA principles into the preliminary design phase of an offshore vessel. The framework is based on the case of a wind farm installation vessel (WTIV). The proposed approach diverges from traditional ship design by treating environmental impact as an important criterion and integrates the LCA into the early design stages, which is a novelty of the sustainability-driven ship design. On the basis of steps usually conducted in the preliminary ship design, a parametric study was conducted to evaluate the life cycle emissions associated with the shipbuilding, maintenance, operation, and dismantling phases. Ship characteristics such as displacement, lightship weight, and main dimensions were correlated with LCA factors, enabling the quantification of emissions at an early design stage with the use of the developed database and statistical regression models. Power demand estimation for different operational scenarios—free-running transit, dynamic positioning, and stationary installation—highlighted the significant contribution of offshore-specific vessel activities to life cycle emissions. The results demonstrate that the operational phases remain the most important contributors to overall emissions, mostly through CO₂ and NO_x production. However, emissions from shipbuilding, maintenance, and dismantling also play a critical role, justifying the need for early design interventions. Our findings highlight the need to integrate LCA into the design spiral for balanced sustainability, efficiency, and feasibility. This study provides a foundation for future research into multi-objective optimization models that incorporate LCA into offshore vessel design. Full article

Apples (Malus domestica Borkh.) represent one of the most widely cultivated and consumed fruits globally, with significant genetic diversity among cultivars. This study aimed to evaluate the morphological, biochemical, and organoleptic characteristics of 34 apple cultivars, including ancient Romanian varieties, internationally old and modern cultivars, and new selections. The assessment was conducted to identify valuable traits for breeding programs and commercial applications. Morphological analysis revealed significant variation in fruit size, shape, and weight, with international ‘classic’ cultivars exhibiting larger dimensions on average. Biochemical profiling indicated notable differences in moisture content, total soluble solids, titratable acidity, and carotenoid levels, with some traditional cultivars demonstrating high nutritional potential. Texture analysis showed variations in peel hardness, flesh firmness, and toughness, influencing storage capacity and consumer preference. Organoleptic evaluations highlighted the superior sensory attributes of cultivars such as ‘Golden Orange’, ‘Jonathan’, ‘Kaltherer Böhmer’, and ‘Golden Delicious’, which ranked highest in terms of taste, aroma, and juiciness. Statistical analyses, including principal component and hierarchical clustering analyses, further distinguished cultivars based on their physicochemical and sensory profiles. The findings emphasize the importance of genetic diversity in apples in maintaining a resilient and sustainable assortment. This study provides valuable insights for breeding programs and for orchard, market, and apple industry development. We also highlight future directions, promoting the conservation and strategic use of both traditional and modern cultivars. Full article

Kinanthropometry is the study of body dimensions and composition measurements, which are influenced by factors such as age and nutritional status, establishing a relationship between static measurements and dynamic performance. This study aimed to evaluate the kinanthropometric differences among 403 individuals (aged 18–42), categorized by biological sex and the recreational sport they practiced. The main objective of this study was to clarify whether or not there were statistically significant differences between these groups. All of the measurements and indices were obtained following the International Society for the Advancement of Kinanthropometry (ISAK) protocol. Significant differences were found in most variables among the different sports. In general, the men showed higher values in terms of weight, height, body circumference, body mass index (BMI), relative index of the lower limbs (RILLs), percentage of muscle mass (%M), and percentage of residual mass (%R). The women exhibited higher values in terms of skinfold thicknesses, Cormic index (CI), body density index (BDI), percentage of fat mass (%F), and percentage of bone mass (%B). These findings can guide individuals in selecting sports based on their morphotype, optimizing their physical performance in recreational activities and improving their health and quality of life. Full article

Background/Objectives: Prader–Willi syndrome (PWS) is a rare, genetically determined neurodevelopmental disorder. Individuals with PWS face numerous challenges that significantly impact their psychological well-being and quality of life, ultimately limiting their personal and social functioning. This study aimed to evaluate the quality of life and psychological well-being in a sample of Italian adult patients with PWS compared to an age-matched control group of normal-weight Italian individuals. Methods: Thirty patients with PWS (11 men and 19 women; mean age ± SD: 36.4 ± 10.31 years; mean Body Mass Index (BMI: 35.7 ± 8.92 kg/m²) and thirty Italian adult individuals from the general population (5 men and 25 women; mean age ± SD: 32.1 ± 6.86 years; mean Body Mass Index (BMI: 21.8 ± 2.90 kg/m²) were studied. Quality of life and well-being were assessed using the Italian versions of the 36-item Health Survey Short Form and the Psychological General Well-Being Index. Results: Normal-weight subjects scored significantly higher than PWS patients on the physical health (p < 0.001) and social functioning (p = 0.047) subscales of the SF-36. Conversely, PWS patients scored higher on the vitality subscale (p < 0.001). Similarly, the vitality subscale of the PGWBI was significantly higher in PWS patients than in controls (p = 0.010), whereas the Self-Control subscale of the PGWBI was higher in controls compared to PWS patients, albeit not statistically significant (p = 0.057). Discussion: Patients with PWS exhibited impairments in various aspects of quality of life and psychological well-being, including physical, behavioral, and social domains. However, the higher vitality scores observed in PWS patients suggest a preserved dimension of their psychological well-being. Conclusions: These findings enhance the understanding of the psychological condition of patients with PWS and provide valuable insights for improving multidisciplinary psychological treatment approaches for these individuals. Full article

Due to the global demand for dimension stones, fast and reliable evaluation tools are essential for assessing the quality of dimension stones. For this reason, this study aims to develop comprehensive tools for estimating the abrasion resistance of various dimension stones from Turkey. Non-destructive rock properties, including dry density (ρ_d), water absorption by weight (w_a), and pulse wave velocity (V_p), were determined to build a comprehensive database for soft computing analyses. Three predictive models were established using multivariate adaptive regression spline (MARS), M5P, and artificial neural networks (ANN) methodologies. The performance of the models was assessed through scatter plots and statistical indicators, showing that the ANN-based model outperforms those based on M5P and MARS. The applicability of the models was further validated with independent data from the existing literature, confirming that all models are suitable for estimating varying Böhme abrasion values (BAVs). A MATLAB-based software tool, called Böhme abrasion calculator (v1.00), was also developed, allowing users to estimate BAV values by inputting adopted non-destructive rock properties. This tool is available upon request, supporting the dimension stone industry and fostering future research in this field. Full article

Remaining useful life (RUL) prediction plays a fundamental role in the prognostics and health management of mechanical equipment. Consequently, extensive research has been devoted to estimating the RUL of mechanical equipment. Owing to the development of modern advanced sensor technologies, a significant amount of monitoring data is recorded. Traditional methods, such as machine-learning-based methods and statistical-data-driven methods, are ineffective in matching when faced with big data thus leading to poor predictions. As a result, deep-learning-based methods are extensively utilized due to their efficient capability to excavate deep features and realize accurate predictions. However, most deep-learning-based methods only provide point estimations and ignore the prediction uncertainty. To address this limitation, this paper proposes a parallel prognostic network to sufficiently excavate the degradation features from multiple dimensions for more accurate RUL prediction. In addition, accurate calculation of model evidence is extremely difficult when dealing with big data so the Monte Carlo dropout is employed to infer the model weights under low computational cost and high scalability to obtain a probabilistic RUL prediction. Finally, the C-MAPSS aero-engine dataset is employed to validate the proposed dual-channel framework. The experimental results illustrate its superior prediction performance compared to other deep learning methods and the ability to quantify prediction uncertainty. Full article